The document summarizes Thomas House's talk on modeling the spread of memes and behaviors online using mathematical models. It discusses Richard Dawkins' concept of a meme, presents the classic SIR model of infectious disease spread, and proposes a new ODE model to capture "complex contagion" where adoption of a behavior depends on multiple exposures. It provides examples showing the model can reproduce fast, super-exponential growth seen in real photo fads spreading on the internet. The talk concludes by examining observational data from Google Trends to test if complex contagion effects can be detected in behaviors spreading in the real world.
This research aims to characterize HIV at-risk populations among men who have sex with men (MSM) in San Diego by analyzing social media data. The researchers collect tweets from San Diego and classify them based on risk categories like drug use, sex venues, etc. They build a social network graph of Twitter users and their connections and compare the structure to the real-world HIV transmission network. Exploratory analysis of the social graph reveals patterns in topics of discussion and network structures that can help predict HIV transmission risk and enable prevention efforts. Future work includes further data collection, interactive visualizations, and computational models to understand how the social network evolves and relates to the sexual network transmitting HIV.
These days considering expansion of networks, dissemination of information has become one of significant cases for researchers. In social networks in addition to social structures and people effectiveness on each other, Profit increase of sales, publishing a news or rumor, spread or diffusion of an idea can be mentioned. In social societies, people affect each other and with an individual’s membership, his friends
may join that group as well. In publishing a piece of news, independent of its nature there are different ways to expand it. Since information isn’t always suitable and positive, this article is trying to introduce the immunization mechanism against this information. The meaning of immunization is a kind of Slow Publishing of such information in network. Therefor it has been tried in this article to slow down the
publishing of information or even stop them. With comparison of presented methods for immunization and also presenting rate delay parameter, the immunization of methods were evaluated and we identified the most effective immunization method. Among existing methods for immunization and recommended methods, recommended methods also have an effective role in preventing spread of malicious rumor.
Expelling Information of Events from Critical Public Space using Social Senso...ijtsrd
Open foundation frameworks give a significant number of the administrations that are basic to the wellbeing, working, and security of society. A considerable lot of these frameworks, in any case, need persistent physical sensor checking to have the option to recognize disappointment occasions or harm that has struck these frameworks. We propose the utilization of social sensor enormous information to recognize these occasions. We center around two primary framework frameworks, transportation and vitality, and use information from Twitter streams to identify harm to spans, expressways, gas lines, and power foundation. Through a three step filtering approach and assignment to geographical cells, we are able to filter out noise in this data to produce relevant geo located tweets identifying failure events. Applying the strategy to real world data, we demonstrate the ability of our approach to utilize social sensor big data to detect damage and failure events in these critical public infrastructures. Samatha P. K | Dr. Mohamed Rafi "Expelling Information of Events from Critical Public Space using Social Sensor Big Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25350.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/25350/expelling-information-of-events-from-critical-public-space-using-social-sensor-big-data/samatha-p-k
Это интереснейшее исследование того, насколько лента новостей Facebook хороша как механика распространения информации (виральности) в Facebook. Ставит под сомнение некоторые известные приемы посева классических вирусов.
Gesundheit! modeling contagion through facebook news feedMaxim Boiko Savenko
This paper analyzes the diffusion of information on Facebook through users' News Feeds. The researchers analyzed data on over 262,000 Facebook Pages and their fans over a 6-month period. They found that while diffusion chains on Facebook can be extremely long, they are typically not the result of single chain reactions, but rather many short diffusion chains merging together. The researchers used statistical models to analyze these diffusion chains and found no evidence that a user's demographics or Facebook activity could predict the length of diffusion chains they initiate.
This document discusses social network analysis and provides examples of social networks. It begins by defining what a social network is - a set of nodes connected by edges that can represent people and their relationships. It then provides examples of social networks from different domains like disease transmission, collaboration networks, and online networks. Key concepts in social network analysis like centrality, clustering, distance, and community structure are introduced. The document emphasizes that network structure can influence outcomes more than individual traits and discusses using network analysis to understand topics like information diffusion and disease spread.
Alluding Communities in Social Networking Websites using Enhanced Quasi-cliqu...IJMTST Journal
1) The document proposes an enhanced technique to recommend communities to users in social networks based on the user's interests and their strong friends.
2) It identifies a user's area of interest by analyzing their posts and classifying keywords. It then determines the user's strong friends based on an enhanced quasi-clique technique, considering interaction strength.
3) Communities are recommended by considering both the user's interests and strong friends. This provides a more precise recommendation than only considering strong friends.
This research aims to characterize HIV at-risk populations among men who have sex with men (MSM) in San Diego by analyzing social media data. The researchers collect tweets from San Diego and classify them based on risk categories like drug use, sex venues, etc. They build a social network graph of Twitter users and their connections and compare the structure to the real-world HIV transmission network. Exploratory analysis of the social graph reveals patterns in topics of discussion and network structures that can help predict HIV transmission risk and enable prevention efforts. Future work includes further data collection, interactive visualizations, and computational models to understand how the social network evolves and relates to the sexual network transmitting HIV.
These days considering expansion of networks, dissemination of information has become one of significant cases for researchers. In social networks in addition to social structures and people effectiveness on each other, Profit increase of sales, publishing a news or rumor, spread or diffusion of an idea can be mentioned. In social societies, people affect each other and with an individual’s membership, his friends
may join that group as well. In publishing a piece of news, independent of its nature there are different ways to expand it. Since information isn’t always suitable and positive, this article is trying to introduce the immunization mechanism against this information. The meaning of immunization is a kind of Slow Publishing of such information in network. Therefor it has been tried in this article to slow down the
publishing of information or even stop them. With comparison of presented methods for immunization and also presenting rate delay parameter, the immunization of methods were evaluated and we identified the most effective immunization method. Among existing methods for immunization and recommended methods, recommended methods also have an effective role in preventing spread of malicious rumor.
Expelling Information of Events from Critical Public Space using Social Senso...ijtsrd
Open foundation frameworks give a significant number of the administrations that are basic to the wellbeing, working, and security of society. A considerable lot of these frameworks, in any case, need persistent physical sensor checking to have the option to recognize disappointment occasions or harm that has struck these frameworks. We propose the utilization of social sensor enormous information to recognize these occasions. We center around two primary framework frameworks, transportation and vitality, and use information from Twitter streams to identify harm to spans, expressways, gas lines, and power foundation. Through a three step filtering approach and assignment to geographical cells, we are able to filter out noise in this data to produce relevant geo located tweets identifying failure events. Applying the strategy to real world data, we demonstrate the ability of our approach to utilize social sensor big data to detect damage and failure events in these critical public infrastructures. Samatha P. K | Dr. Mohamed Rafi "Expelling Information of Events from Critical Public Space using Social Sensor Big Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25350.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/25350/expelling-information-of-events-from-critical-public-space-using-social-sensor-big-data/samatha-p-k
Это интереснейшее исследование того, насколько лента новостей Facebook хороша как механика распространения информации (виральности) в Facebook. Ставит под сомнение некоторые известные приемы посева классических вирусов.
Gesundheit! modeling contagion through facebook news feedMaxim Boiko Savenko
This paper analyzes the diffusion of information on Facebook through users' News Feeds. The researchers analyzed data on over 262,000 Facebook Pages and their fans over a 6-month period. They found that while diffusion chains on Facebook can be extremely long, they are typically not the result of single chain reactions, but rather many short diffusion chains merging together. The researchers used statistical models to analyze these diffusion chains and found no evidence that a user's demographics or Facebook activity could predict the length of diffusion chains they initiate.
This document discusses social network analysis and provides examples of social networks. It begins by defining what a social network is - a set of nodes connected by edges that can represent people and their relationships. It then provides examples of social networks from different domains like disease transmission, collaboration networks, and online networks. Key concepts in social network analysis like centrality, clustering, distance, and community structure are introduced. The document emphasizes that network structure can influence outcomes more than individual traits and discusses using network analysis to understand topics like information diffusion and disease spread.
Alluding Communities in Social Networking Websites using Enhanced Quasi-cliqu...IJMTST Journal
1) The document proposes an enhanced technique to recommend communities to users in social networks based on the user's interests and their strong friends.
2) It identifies a user's area of interest by analyzing their posts and classifying keywords. It then determines the user's strong friends based on an enhanced quasi-clique technique, considering interaction strength.
3) Communities are recommended by considering both the user's interests and strong friends. This provides a more precise recommendation than only considering strong friends.
Massively Parallel Simulations of Spread of Infectious Diseases over Realisti...Subhajit Sahu
Highlighted notes while preparing for project on Computational Epidemics:
Massively Parallel Simulations of Spread of Infectious Diseases over Realistic Social Networks
Abhinav Bhatele, Jae-Seung Yeom, Nikhil Jain, Chris J. Kuhlman, Yarden Livnat, Keith R. Bisset, Laxmikant V. Kale, Madhav V. Marathe
Controlling the spread of infectious diseases in large populations is an important societal challenge. Mathematically, the problem is best captured as a certain class of reactiondiffusion processes (referred to as contagion processes) over appropriate synthesized interaction networks. Agent-based models have been successfully used in the recent past to study such contagion processes. We describe EpiSimdemics, a highly scalable, parallel code written in Charm++ that uses agent-based modeling to simulate disease spreads over large, realistic, co-evolving interaction networks. We present a new parallel implementation of EpiSimdemics that achieves unprecedented strong and weak scaling on different architectures — Blue Waters, Cori and Mira. EpiSimdemics achieves five times greater speedup than the second fastest parallel code in this field. This unprecedented scaling is an important step to support the long term vision of realtime epidemic science. Finally, we demonstrate the capabilities of EpiSimdemics by simulating the spread of influenza over a realistic synthetic social contact network spanning the continental United States (∼280 million nodes and 5.8 billion social contacts).
Massively Parallel Simulations of Spread of Infectious Diseases over Realisti...Subhajit Sahu
Highlighted notes while studying for project work:
Massively Parallel Simulations of Spread of Infectious Diseases over Realistic Social Networks
Abhinav Bhatele†
Jae-Seung Yeom†
Nikhil Jain†
Chris J. Kuhlman∗
Yarden Livnat‡
Keith R. Bisset∗
Laxmikant V. Kale§
Madhav V. Marathe∗
†Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, California 94551 USA
∗Biocomplexity Institute & Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24061 USA
‡Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah 84112 USA
§Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 USA
E-mail: †{bhatele, yeom2, nikhil}@llnl.gov, ∗{ckuhlman, kbisset, mmarathe}@vbi.vt.edu
Abstract—Controlling the spread of infectious diseases in large populations is an important societal challenge. Mathematically, the problem is best captured as a certain class of reactiondiffusion processes (referred to as contagion processes) over appropriate synthesized interaction networks. Agent-based models have been successfully used in the recent past to study such contagion processes. We describe EpiSimdemics, a highly scalable, parallel code written in Charm++ that uses agent-based modeling to simulate disease spreads over large, realistic, co-evolving interaction networks. We present a new parallel implementation of EpiSimdemics that achieves unprecedented strong and weak scaling on different architectures — Blue Waters, Cori and Mira. EpiSimdemics achieves five times greater speedup than the second fastest parallel code in this field. This unprecedented scaling is an important step to support the long term vision of realtime epidemic science. Finally, we demonstrate the capabilities of EpiSimdemics by simulating the spread of influenza over a realistic synthetic social contact network spanning the continental United States (∼280 million nodes and 5.8 billion social contacts).
Modeling Spread of Disease from Social InteractionsPrashanth Selvam
This document summarizes a research paper that models the spread of disease through social interactions on Twitter. It used machine learning techniques to analyze over 2.5 million geo-tagged Twitter messages to detect illness-related messages and identify social ties to infected people. A support vector machine classifier was able to accurately detect illness tweets. The model also found that social ties and co-location with infected individuals increases the likelihood of contracting an illness. This work provides valuable insights into predicting disease spread through social media data without active user participation.
A general stochastic information diffusion model in social networks based on ...IJCNCJournal
Social networks are an important infrastructure for information, viruses and innovations propagation. Since users’
behavior has influenced by other users’ activity, some groups of people would be made regard to similarity of users’
interests. On the other hand, dealing with many events in real worlds, can be justified in social networks; spreading
disease is one instance of them. People’s manner and infection severity are more important parameters in
dissemination of diseases. Both of these reasons derive, whether the diffusion leads to an epidemic or not. SIRS is a
hybrid model of SIR and SIS disease models to spread contamination. A person in this model can be returned to
susceptible state after it removed. According to communities which are established on the social network, we use the
compartmental type of SIRS model. During this paper, a general compartmental information diffusion model would
be proposed and extracted some of the beneficial parameters to analyze our model. To adapt our model to realistic
behaviors, we use Markovian model, which would be helpful to create a stochastic manner of the proposed model.
In the case of random model, we can calculate probabilities of transaction between states and predicting value of
each state. The comparison between two mode of the model shows that, the prediction of population would be
verified in each state.
Information, Knowledge Management & Coordination Systems: Complex Systems App...CITE
Date: 4 Jun 2013
Time: 12:45pm - 2:00pm
Venue: Room 101, Runme Shaw Building, The University of Hong Kong
Speakers: Professor Liaquat Hossain, University of Sydney
------------------------------------
http://www.cite.hku.hk/news.php?id=502&category=conference
The document discusses using an epidemic simulation model called CASMIM (Cellular Automata with Social Mirror Identity Model) to better model real-world epidemics and evaluate public health policies. CASMIM uses a social mirror identity concept where each individual is represented by multiple identities to model their movement and interactions. It was found to generate small-world network structures similar to human social networks and allow sensitivity analysis of factors like population size, individual diversity, and neighborhood type.
This document discusses using social network analysis to design and implement behavior change interventions. It begins by outlining key network concepts like diffusion of innovations and mathematical models of diffusion. It then discusses how social networks influence behaviors through concepts like network exposure, tie strength, and thresholds. The document concludes by describing how to use social network analysis at different stages of intervention including needs assessment, program design, implementation, and monitoring through approaches like network ethnography, identifying opinion leaders, and using network diagnostics.
Community Finding with Applications on Phylogenetic Networks [Extended Abstract]Luís Rita
[Master Thesis Extended Abstract]
With the advent of high-throughput sequencing methods, new ways of visualizing and analyzing increasingly amounts of data are needed. Although some software already exist, they do not scale well or require advanced skills to be useful in phylogenetics.
The aim of this thesis was to implement three community finding algorithms – Louvain, Infomap and Layered Label Propagation (LLP); to benchmark them using two synthetic networks – Girvan-Newman (GN) and Lancichinetti-Fortunato-Radicchi (LFR); to test them in real networks, particularly, in one derived from a Staphylococcus aureus MLST dataset; to compare visualization frameworks – Cytoscape.js and D3.js, and, finally, to make it all available online (mscthesis.herokuapp.com).
Louvain, Infomap and LLP were implemented in JavaScript. Unless otherwise stated, next conclusions are valid for GN and LFR. In terms of speed, Louvain outperformed all others. Considering accuracy, in networks with well-defined communities, Louvain was the most accurate. For higher mixing, LLP was the best. Contrarily to weakly mixed, it is advantageous to increase the resolution parameter in highly mixed GN. In LFR, higher resolution decreases the accuracy of detection, independently of the mixing parameter. The increase of the average node degree enhanced partitioning accuracy and suggested detection by chance was minimized. It is computationally more intensive to generate GN with higher mixing or average degree, using the algorithm developed in the thesis or the LFR implementation. In S. aureus network, Louvain was the fastest and the most accurate in detecting the clusters of seven groups of strains directly evolved from the common ancestor.
A computational model of computer virus propagationUltraUploader
1) A computational model is developed to simulate the propagation of computer viruses and warning messages within organizational social and computer networks.
2) The model represents the networks as graphs and incorporates mechanisms of virus propagation, node state transitions, and the dissemination of warning messages.
3) Experiments show that random graphs with similar characteristics to real-world networks can model social networks, and isolating organizations may prevent virus infection but also limit receipt of important warning messages.
Information Contagion through Social Media: Towards a Realistic Model of the ...Axel Bruns
Paper by Axel Bruns, Patrik Wikström, Peta Mitchell, Brenda Moon, Felix Münch, Lucia Falzon, and Lucy Resnyansky presented at the ACSPRI 2016 conference, Sydney, 19-22 July 2016/
Fattori - 50 abstracts of e patient. In collaborazione con Monica DaghioGiuseppe Fattori
This document contains summaries of 50 abstracts related to e-patients and social media. Some key points:
1) Participatory surveillance of hypoglycemia in an online diabetes social network found high rates of hypoglycemic events and related harms like daily worry and withdrawal from activities. Engagement was also high.
2) Analysis of self-reported Parkinson's disease symptom data from an online platform found short-term dynamics like fluctuations exceeding clinically important differences that add to understanding of disease progression.
3) Examination of influential cancer patients on Twitter found most tweets focused on support rather than medical information, indicating its role in online patient community and support.
Probabilistic models for anomaly detection based on usage of network trafficAlexander Decker
This document discusses probabilistic models for anomaly detection based on network traffic usage. It introduces several probabilistic methods and statistical models that can be used for network traffic anomaly detection, including Bayesian theorem, mean and standard deviation models, point and interval estimations, multivariate regression models, Markov processes, and time series models. As an example, it describes modeling the spread of computer worms using epidemiological models such as linear, exponential, logistic, and differential equation models. It also discusses the different possible scenarios an intrusion detection system can encounter and how to calculate probabilities of outcomes using Bayesian theorem.
Builder.ai's CEO and head of AI have developed a framework that looks at the pandemic and formulates an approach to spread detection, digital passports and vaccine delivery. It's released under creative commons for everyone to leverage.
This document summarizes open problems and future directions in the field of social networks and health. It identifies key areas for methodological development including dynamic diffusion models, improved community detection techniques, and understanding triadic network structures. Important theoretical advances involve modeling multiplex and evolving networks over time as well as better understanding social mechanisms linking networks to health. Future data collection should incorporate electronic traces, return to community-based studies, and develop national samples capturing full network contexts.
Networks provide connections and positions that influence health outcomes. Social network analysis examines relationships between actors to understand how networks impact behavior. Networks matter through both connectionist mechanisms like diffusion, and positional mechanisms like social roles. Network data can be analyzed at different levels from individual ego networks to global networks, and can involve one or multiple types of relationships between nodes. Social network data is commonly represented through matrices and lists to encode network structure and allow computational analysis.
IRJET- Fake News Detection and Rumour Source IdentificationIRJET Journal
This document discusses methods for detecting fake news and identifying the source of rumors on social media. It proposes using Bayesian classification to classify information into real or fake categories based on the outputs. If the combined outputs from the classes do not match, then the information is considered fake. It also discusses using a reverse dissemination strategy to identify a group of suspects for the original rumor source, rather than examining each individual. This addresses issues with identifying sources. The method aims to identify the source node based on which nodes have accepted the rumor. Machine learning and natural language processing techniques are used to detect fake news from article content.
Mathematical Models of the Spread of Diseases, Opinions, Information, and Mis...Mason Porter
This is my general-audience talk at DiscCon III (2021 WorldCon).
My talk overlapped with the Hugo Award ceremony, but the video will be posted later on the DisCon website for attendees who want to see it.
Networks & Health
This document provides an introduction and overview of social network analysis and its relevance to health research. It discusses key concepts such as what networks are, different types of network data including one-mode and two-mode data, and different levels of analysis including ego networks, partial networks, and complete networks. The document also discusses why networks matter for health through connectionist mechanisms like diffusion and positional mechanisms like social roles. Overall, the document serves as a high-level introduction to social network concepts and their application to health research.
Assessment of the main features of the model of dissemination of information ...IJECEIAES
Social networks provide a fairly wide range of data that allows one way or another to evaluate the effect of the dissemination of information. This article presents the results of a study that describes methods for determining the key parameters of the model needed to analyze and predict the dissemination of information in social networks. An approach based on the analysis of statistical data on user behavior in social networks is proposed. The process of evaluating the main features of the model is described, including the mathematical methods used for data analysis and information dissemination modeling. The study aims to understand the processes of information dissemination in social networks and develop recommendations for the effective use of social networks as a communication and brand promotion tool, as well as to consider the analytical properties of the classical susceptible-infected-removed (SIR) model and evaluate its applicability to the problem of information dissemination. The results of the study can be used to create algorithms and techniques that will effectively manage the process of information dissemination in social networks.
Massively Parallel Simulations of Spread of Infectious Diseases over Realisti...Subhajit Sahu
Highlighted notes while preparing for project on Computational Epidemics:
Massively Parallel Simulations of Spread of Infectious Diseases over Realistic Social Networks
Abhinav Bhatele, Jae-Seung Yeom, Nikhil Jain, Chris J. Kuhlman, Yarden Livnat, Keith R. Bisset, Laxmikant V. Kale, Madhav V. Marathe
Controlling the spread of infectious diseases in large populations is an important societal challenge. Mathematically, the problem is best captured as a certain class of reactiondiffusion processes (referred to as contagion processes) over appropriate synthesized interaction networks. Agent-based models have been successfully used in the recent past to study such contagion processes. We describe EpiSimdemics, a highly scalable, parallel code written in Charm++ that uses agent-based modeling to simulate disease spreads over large, realistic, co-evolving interaction networks. We present a new parallel implementation of EpiSimdemics that achieves unprecedented strong and weak scaling on different architectures — Blue Waters, Cori and Mira. EpiSimdemics achieves five times greater speedup than the second fastest parallel code in this field. This unprecedented scaling is an important step to support the long term vision of realtime epidemic science. Finally, we demonstrate the capabilities of EpiSimdemics by simulating the spread of influenza over a realistic synthetic social contact network spanning the continental United States (∼280 million nodes and 5.8 billion social contacts).
Massively Parallel Simulations of Spread of Infectious Diseases over Realisti...Subhajit Sahu
Highlighted notes while studying for project work:
Massively Parallel Simulations of Spread of Infectious Diseases over Realistic Social Networks
Abhinav Bhatele†
Jae-Seung Yeom†
Nikhil Jain†
Chris J. Kuhlman∗
Yarden Livnat‡
Keith R. Bisset∗
Laxmikant V. Kale§
Madhav V. Marathe∗
†Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, California 94551 USA
∗Biocomplexity Institute & Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24061 USA
‡Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah 84112 USA
§Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 USA
E-mail: †{bhatele, yeom2, nikhil}@llnl.gov, ∗{ckuhlman, kbisset, mmarathe}@vbi.vt.edu
Abstract—Controlling the spread of infectious diseases in large populations is an important societal challenge. Mathematically, the problem is best captured as a certain class of reactiondiffusion processes (referred to as contagion processes) over appropriate synthesized interaction networks. Agent-based models have been successfully used in the recent past to study such contagion processes. We describe EpiSimdemics, a highly scalable, parallel code written in Charm++ that uses agent-based modeling to simulate disease spreads over large, realistic, co-evolving interaction networks. We present a new parallel implementation of EpiSimdemics that achieves unprecedented strong and weak scaling on different architectures — Blue Waters, Cori and Mira. EpiSimdemics achieves five times greater speedup than the second fastest parallel code in this field. This unprecedented scaling is an important step to support the long term vision of realtime epidemic science. Finally, we demonstrate the capabilities of EpiSimdemics by simulating the spread of influenza over a realistic synthetic social contact network spanning the continental United States (∼280 million nodes and 5.8 billion social contacts).
Modeling Spread of Disease from Social InteractionsPrashanth Selvam
This document summarizes a research paper that models the spread of disease through social interactions on Twitter. It used machine learning techniques to analyze over 2.5 million geo-tagged Twitter messages to detect illness-related messages and identify social ties to infected people. A support vector machine classifier was able to accurately detect illness tweets. The model also found that social ties and co-location with infected individuals increases the likelihood of contracting an illness. This work provides valuable insights into predicting disease spread through social media data without active user participation.
A general stochastic information diffusion model in social networks based on ...IJCNCJournal
Social networks are an important infrastructure for information, viruses and innovations propagation. Since users’
behavior has influenced by other users’ activity, some groups of people would be made regard to similarity of users’
interests. On the other hand, dealing with many events in real worlds, can be justified in social networks; spreading
disease is one instance of them. People’s manner and infection severity are more important parameters in
dissemination of diseases. Both of these reasons derive, whether the diffusion leads to an epidemic or not. SIRS is a
hybrid model of SIR and SIS disease models to spread contamination. A person in this model can be returned to
susceptible state after it removed. According to communities which are established on the social network, we use the
compartmental type of SIRS model. During this paper, a general compartmental information diffusion model would
be proposed and extracted some of the beneficial parameters to analyze our model. To adapt our model to realistic
behaviors, we use Markovian model, which would be helpful to create a stochastic manner of the proposed model.
In the case of random model, we can calculate probabilities of transaction between states and predicting value of
each state. The comparison between two mode of the model shows that, the prediction of population would be
verified in each state.
Information, Knowledge Management & Coordination Systems: Complex Systems App...CITE
Date: 4 Jun 2013
Time: 12:45pm - 2:00pm
Venue: Room 101, Runme Shaw Building, The University of Hong Kong
Speakers: Professor Liaquat Hossain, University of Sydney
------------------------------------
http://www.cite.hku.hk/news.php?id=502&category=conference
The document discusses using an epidemic simulation model called CASMIM (Cellular Automata with Social Mirror Identity Model) to better model real-world epidemics and evaluate public health policies. CASMIM uses a social mirror identity concept where each individual is represented by multiple identities to model their movement and interactions. It was found to generate small-world network structures similar to human social networks and allow sensitivity analysis of factors like population size, individual diversity, and neighborhood type.
This document discusses using social network analysis to design and implement behavior change interventions. It begins by outlining key network concepts like diffusion of innovations and mathematical models of diffusion. It then discusses how social networks influence behaviors through concepts like network exposure, tie strength, and thresholds. The document concludes by describing how to use social network analysis at different stages of intervention including needs assessment, program design, implementation, and monitoring through approaches like network ethnography, identifying opinion leaders, and using network diagnostics.
Community Finding with Applications on Phylogenetic Networks [Extended Abstract]Luís Rita
[Master Thesis Extended Abstract]
With the advent of high-throughput sequencing methods, new ways of visualizing and analyzing increasingly amounts of data are needed. Although some software already exist, they do not scale well or require advanced skills to be useful in phylogenetics.
The aim of this thesis was to implement three community finding algorithms – Louvain, Infomap and Layered Label Propagation (LLP); to benchmark them using two synthetic networks – Girvan-Newman (GN) and Lancichinetti-Fortunato-Radicchi (LFR); to test them in real networks, particularly, in one derived from a Staphylococcus aureus MLST dataset; to compare visualization frameworks – Cytoscape.js and D3.js, and, finally, to make it all available online (mscthesis.herokuapp.com).
Louvain, Infomap and LLP were implemented in JavaScript. Unless otherwise stated, next conclusions are valid for GN and LFR. In terms of speed, Louvain outperformed all others. Considering accuracy, in networks with well-defined communities, Louvain was the most accurate. For higher mixing, LLP was the best. Contrarily to weakly mixed, it is advantageous to increase the resolution parameter in highly mixed GN. In LFR, higher resolution decreases the accuracy of detection, independently of the mixing parameter. The increase of the average node degree enhanced partitioning accuracy and suggested detection by chance was minimized. It is computationally more intensive to generate GN with higher mixing or average degree, using the algorithm developed in the thesis or the LFR implementation. In S. aureus network, Louvain was the fastest and the most accurate in detecting the clusters of seven groups of strains directly evolved from the common ancestor.
A computational model of computer virus propagationUltraUploader
1) A computational model is developed to simulate the propagation of computer viruses and warning messages within organizational social and computer networks.
2) The model represents the networks as graphs and incorporates mechanisms of virus propagation, node state transitions, and the dissemination of warning messages.
3) Experiments show that random graphs with similar characteristics to real-world networks can model social networks, and isolating organizations may prevent virus infection but also limit receipt of important warning messages.
Information Contagion through Social Media: Towards a Realistic Model of the ...Axel Bruns
Paper by Axel Bruns, Patrik Wikström, Peta Mitchell, Brenda Moon, Felix Münch, Lucia Falzon, and Lucy Resnyansky presented at the ACSPRI 2016 conference, Sydney, 19-22 July 2016/
Fattori - 50 abstracts of e patient. In collaborazione con Monica DaghioGiuseppe Fattori
This document contains summaries of 50 abstracts related to e-patients and social media. Some key points:
1) Participatory surveillance of hypoglycemia in an online diabetes social network found high rates of hypoglycemic events and related harms like daily worry and withdrawal from activities. Engagement was also high.
2) Analysis of self-reported Parkinson's disease symptom data from an online platform found short-term dynamics like fluctuations exceeding clinically important differences that add to understanding of disease progression.
3) Examination of influential cancer patients on Twitter found most tweets focused on support rather than medical information, indicating its role in online patient community and support.
Probabilistic models for anomaly detection based on usage of network trafficAlexander Decker
This document discusses probabilistic models for anomaly detection based on network traffic usage. It introduces several probabilistic methods and statistical models that can be used for network traffic anomaly detection, including Bayesian theorem, mean and standard deviation models, point and interval estimations, multivariate regression models, Markov processes, and time series models. As an example, it describes modeling the spread of computer worms using epidemiological models such as linear, exponential, logistic, and differential equation models. It also discusses the different possible scenarios an intrusion detection system can encounter and how to calculate probabilities of outcomes using Bayesian theorem.
Builder.ai's CEO and head of AI have developed a framework that looks at the pandemic and formulates an approach to spread detection, digital passports and vaccine delivery. It's released under creative commons for everyone to leverage.
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The Mathematics of Memes
1. The Mathematics
of Memes
Thomas House
School of Mathematics, University of Manchester
Galois Group Talk
Simon 2.39
1pm 5 December 2017
2. How I Ended up Giving This Talk
• Veronica Kelsey Sent me this …
3. What is a Meme?
• The modern usage involves only the internet,
but the word goes back to Dawkins:
4. Modelling literally viral
behaviour: the SIR model
• The ‘SIR model’ has two parameters:
– R0 = β/γ, the average number of secondary cases
produced by an index case early in the epidemic
(more on this later).
– T=1/γ, the average time cases spend infectious.
• As an ODE:
dS
dt
= −βSI ,
dI
dt
= βSI −γI .
5. An SIR epidemic
The SIR model
does
reproduce the
‘up and down’
behaviour seen
in infectious
disease
epidemics
0 20 40 60 80
0
0.2
0.4
0.6
0.8
1
Time (days)
ProportionofPopulation
Susceptible Infectious Recovered
The code to produce this figure and similar output is available on my
website. Parameter choices are R0 = 3; T = 4 days.
7. 1918-19 H1N1 Influenza, England & Wales
0 5 10 15 20
0
1
2
x 10
4
Reporteddeaths
Week
0 5 10 15 20
0
5
10
x 10
6
Modelledinfluenzacases
Source: House (2012), Cont. Phys.
8. 2002 West Nile Virus, USA
Source: Huhn et al. (2003) AFP.
data through ArboNET, a secure, Web-based
surveillance network comprising 54 state and
local public health departments. Local health
quito. In the United States, the virus is main-
tained in an enzootic mosquito-bird-mos-
quito cycle that primarily involves Culex mos-
FIGURE 2. Human West Nile meningitis and encephalitis cases in 2002, by location and time of illness
onset. As of April 15, 2003, there were 4,156 reported cases. Southern states included Alabama,
Arkansas, California, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Mary-
land, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, West Virginia, and Vir-
ginia. Northern states included Colorado, Connecticut, Illinois, Indiana, Iowa, Kansas, Massachusetts,
Michigan, Minnesota, Missouri, Montana, Nebraska, New Jersey, New York, North Dakota, Ohio,
Pennsylvania, Rhode Island, South Dakota, Vermont, Wisconsin, and Wyoming.
Unpublished data compiled by ArboNET. Centers for Disease Control and Prevention, Center for Infectious Dis-
eases, Division of Vector-Borne Infectious Diseases, Fort Collins, Colo.
West Nile Meningitis and Encephalitis Cases
May25
Jun8
Jun22
Jul6
Jul20
Aug3
Aug17
Aug31
Sep14
Sep28
Oct12
Oct26
Nov9
Nov23
Dec7
Dec21
Week ending
Numberofcases
500
400
300
200
100
0
■ North
■■ South
9. Early Behaviour
Feature 1:
Early exponential
growth in
infection
0 20 40 60 80
0
0.2
0.4
0.6
0.8
1
Time (days)
ProportionofPopulation
Susceptible Infectious Recovered
16 18 20 22 24 26
0
0.05
0.1
0.15
0.2
0.25
Time (days)
ProportionofPopulation
Susceptible Infectious Recovered
10. Epidemic Peak
Feature 2:
The epidemic
peaks when herd
immunity is
reached
0 20 40 60 80
0
0.2
0.4
0.6
0.8
1
Time (days)
ProportionofPopulation
Susceptible Infectious Recovered
26 27 28 29 30 31 32
0.2
0.25
0.3
0.35
0.4
Time (days)
ProportionofPopulation
Susceptible Infectious Recovered
11. Late Behaviour
Feature 3:
Every epidemic
leaves a pool of
susceptibles still
vulnerable to new
outbreaks
0 20 40 60 80
0
0.2
0.4
0.6
0.8
1
Time (days)
ProportionofPopulation
Susceptible Infectious Recovered
80 85 90 95
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
Time (days)
ProportionofPopulation
Susceptible Infectious Recovered
13. Centola
Complex
Contation
• More recent
evidence looked
at a controlled
social network
G. Hunt, A. Miller, T. Olszewski, and P. Wagner for their
suggestions; M. Kosnik and A. Miller for reviews; and
M. Foote for verifying that my subsampling algorithms
were programmed correctly. Numerous contributors to the
Paleobiology Database made this study possible, and
I am particularly grateful to M. Clapham, A. Hendy, and
W. Kiessling for recent contributions. Research described
here was funded by donations from anonymous private
individuals having no connection to it. This is Paleobiology
Database publication 117.
Supporting Online Material
www.sciencemag.org/cgi/content/full/329/5996/1191/DC1
Materials and Methods
Figs. S1 to S9
Tables S1 and S2
References
22 March 2010; accepted 30 June 2010
10.1126/science.1189910
The Spread of Behavior in an Online
Social Network Experiment
Damon Centola
How do social networks affect the spread of behavior? A popular hypothesis states that networks
with many clustered ties and a high degree of separation will be less effective for behavioral
diffusion than networks in which locally redundant ties are rewired to provide shortcuts across the
social space. A competing hypothesis argues that when behaviors require social reinforcement, a
network with more clustering may be more advantageous, even if the network as a whole has a
larger diameter. I investigated the effects of network structure on diffusion by studying the spread
of health behavior through artificially structured online communities. Individual adoption was
much more likely when participants received social reinforcement from multiple neighbors
in the social network. The behavior spread farther and faster across clustered-lattice networks than
across corresponding random networks.
M
any behaviors spread through social
contact (1–3). As a result, the network
structure of who is connected to whom
through social networks, an empirical test of
these predictions has not been possible, because
it requires the ability to independently vary the
friends who may have also signed up for the study
(or from trying to contact health buddies outside
the context of the experiment), I blinded the
identifiers that people used. Participants made
decisions about whether or not to adopt a health
behavior based on the adoption patterns of their
health buddies. The health behavior used for this
study was the decision to register for an Internet-
based health forum, which offered access and rat-
ing tools for online health resources (13).
The health forum was not known (or acces-
sible) to anyone except participants in the ex-
periment. This ensured that the only sources of
encouragement that participants had to join the
forum were the signals that they received from their
health buddies. The forum was populated with ini-
tial ratings to provide content for the early adopters.
However, all subsequent content was contributed
by the participants who joined the forum.
Participants arriving to the study were randomly
assigned to one of two experimental conditions—
REPORTS
http://scieDownloadedfrom
The Spread of Behavior in an Online Social Network Experiment
Damon Centola
DOI: 10.1126/science.1185231
(5996), 1194-1197.329Science
each other, as well as yourself).
many other people who have already adopted the behavior (for example, in the circumstances where your friends know
clustered ones. Certain types of behavior within human systems are thus more likely to spread if people are exposed to
that were signed up for the forum. The behavior spread more readily on clustered networks than on random, poorly
individuals choosing to register for a health forum could be influenced by an artificially constructed network of neighbors
(p. 1194) examined whether the number ofCentoladramatically affect the diffusion of behavior through a population.
interventions) and promote behavior change most effectively across a population. The structure of a social network can
An important question for policy-makers is how to communicate information (for example, about public health
Join the Club
ARTICLE TOOLS http://science.sciencemag.org/content/329/5996/1194
MATERIALS
SUPPLEMENTARY http://science.sciencemag.org/content/suppl/2010/08/31/329.5996.1194.DC1
CONTENT
RELATED http://science.sciencemag.org/content/sci/329/5996/1219.2.full
REFERENCES
http://science.sciencemag.org/content/329/5996/1194#BIBL
This article cites 20 articles, 4 of which you can access for free
PERMISSIONS http://www.sciencemag.org/help/reprints-and-permissions
15. An ODE model of Complex
Contagion
• I considered a model with these ingredients
of
tative
online
more
than
s pro-
atures
epide-
ioural
el;
depends on B(t) in addition to other static parameters).
We assume that individuals with m canvassed neigh-
bours who are engaging in the behaviour commence at
a rate tm or cease at a rate gm as appropriate for their
current behaviour state. The dynamical system for be-
haviour prevalence in the population at time t is then
_BðtÞ ¼
Xn
m¼0
DmðtÞðð1 À BðtÞÞtm À BðtÞgmÞ: ð2:1Þ
To specify an integrable system, it is then necessary
to define a form for the dynamical parameters tm, gm
and a process for the generation of the proportion Dm.
2.2. Dynamical parameters
We now choose a form for the vectors (tm), (gm). It is
2.3. Canvassing method
To complete our model description, we need a form for
the proportion Dm. The simplest assumption is that
there are n independent trials with each trial having
probability B(t), meaning that
Dm ¼ Binðmjn; BðtÞÞ; ð2:3Þ
where Bin() is a binomial probability mass function as
defined in appendix A. This is interpreted as each indi-
2 Report. Modelling behavioural contagion T. House
http://rsif.royalsocieDownloaded from
lation, whereas here dynamics remain Markovian b
the population samples are potentially dependent.
For opinion dynamics, motivated by a comprehe
sive review of the literature and compelling empiric
evidence [2,4], we expect an S-shaped curve for t
response of behavioural transmission probability
the number of encounters with a behaviour. For simp
city, the limiting case of such a curve is taken so tha
tm ¼
t if m ! a;
0 otherwise:
ð2:
This complex form for transmission has not yet be
included in other dynamical systems models of beha
iour spread, and is the main benefit of the modellin
approach considered here. We assume for simplici
16. Fast Growth!
• Such models exhibit very fast growth.initial number I(0) participating in the fad; we will also assume that J(0) = R(0) = 0 and so the
rest of the population is initially in the S compartment so that S(0) = N − I(0).
We can also now make our verbal argument above about ‘excitable’ models more quantita-
tively. Consider the special case of our models in which C = τi = 2 and ✏ = 0. Early in the epi-
demic, for the simple contagion model, making the special choices βi = 1/N and I(0) = 1 for
simplicity, we will be able to make the large-N approximation
dI
dt
⇡ I ) IÖtÜ ⇡ et
; Ö6Ü
i.e. exponential early growth. For the complex contagion model, making the special choices
β = N and I(0) = 1 for simplicity, we will have the large-N approximation
dI
dt
⇡ I2
) IÖtÜ ⇡
1
1 t
; Ö7Ü
which represents super-exponential early growth. In both the simple and complex models I(t)
will eventually stop growing due to non-linear effects as S(t) decreases, but the early growth of
the complex model will be much more ‘explosive’, which is a feature that we will see in real
data.
Evidence for complex contagion
17. Looking for Observational Evidence
• If these are real effects then they ought to be
visible in observational data – i.e. how people
behave ‘in the wild’
• This would have implications for design of
public health interventions (as well as
advertising etc.)
• We sought to do this statistically
18.
19. Testing in the real world – Photo Fads
• Photo-fads like ‘planking’ are spread online
• The involve real-world behaviour
• And as such, they are a ‘pure signal’ for
behaviour
• We looked at ‘Google Trends’ data and fitted
different models to it